In the last two decades, significant efforts have been expended on improving the performance of volumetric rendering. Conventionally, single volume rendering can achieve interactive frame rates on a commodity PC platform with or without hardware acceleration. Research, however, has not concentrated on multiple volume renderings with different orientation, size and resolutions. Conventional methods usually attempt to register and resample the different volumes so that they have the same orientation, size and resolutions. The preprocessing and memory overhead for registering and resampling are sometime quite large. Re-sampling will often time reduce the quality of the original data.
The data generated by modern medical image devices has expanded very quickly. For example, multi-slice Computed Tomography, (hereinafter “CT”) scans can generate a several thousand slice dataset in minutes. Conventional methods for reading each slice by a radiologist is not practical and volume rendering of the data set is needed for understanding and diagnosing this kind of complex dataset.
Using one modality to diagnose a dataset is no longer enough for evaluative purposes. There is an increasing demand for multi-modality fused volume rendering. Examples include using CT or MR data together with PET and SPECT to locate small tumors that appear when conjugating anatomical information with physiological abnormalities. Additional potential uses of data may include fusing MR angiography with MR brain data to allow the physician to predict eventual cerebral damage produced by vascular accidents. Another potential use example involves fusing CT datasets obtained from the same scanner for the same patient but at different treatment stages to determine the effectiveness of the certain cancer treatment.
Conventional methods for fusing data either assume that there are no overlapping regions between multiple volumes and therefore it essentially equals single volume rendering or they require all the volume to be aligned and have the same resolution. Usually a reference volume with the finest resolution is identified as the reference volume and all the other volumes need to register and resample accordingly so that they can be aligned and have the same resolution. This process requires more memory than the original datasets and consequently needs a long preprocessing time. These methods use brute force ray-casting algorithms without optimizing for performance. Ray-Casting without performance optimization is a slow process. Most effective optimization mechanisms involve space leaping and early ray termination techniques.
Graphics hardware and bricking mechanisms may render multiple arbitrarily oriented volumes with different sizes and resolutions effectively.
There is therefore a need to provide a method and apparatus to perform accurate multi-volume rendering without the need to expensive graphics hardware enhancement devices.
There is also a need to provide a method and apparatus to perform these renderings with minimal input from an individual using the diagnostic equipment.
There is a further need to provide a method and apparatus to use ray-acceleration technologies to enhance rendering speed and accuracy.